code: def test_stable_diffusion_upscale_pipeline_fp16(self):
      image = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"
      )
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat_fp16.npy"
      )
  
      model_id = "stabilityai/stable-diffusion-x4-upscaler"
      pipe = StableDiffusionUpscalePipeline.from_pretrained(
          model_id,
          torch_dtype=torch.float16,
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
      pipe.enable_attention_slicing()
  
      prompt = "a cat sitting on a park bench"
  
      generator = torch.manual_seed(0)
      output = pipe(
          prompt=prompt,
          image=image,
          generator=generator,
          output_type="np",
      )
      image = output.images[0]
  
      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 5e-1
  
imports: import gc
  import random
  import tempfile
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 3 for calling the StableDiffusionUpscalePipelineIntegrationTests obj's underlying member method code with example usage: obj.test_stable_diffusion_upscale_pipeline_fp16() without return types